Sampling and Analysis of Animal Scent Signals

Summary

We have developed an effective methodology for sampling and analysis of odor signals in order to understand how they may be used in animal communication. Particularly, we use headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry to analyze the volatile components of animal odors and scent-markings.

Abstract

We have developed an effective methodology for sampling and analysis of odor signals, by using headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry, to understand how they may be used in animal communication. This technique allows the semi-quantitative analysis of the volatile components of odor secretions by enabling the separation and tentative identification of the components in the sample, followed by the analysis of peak area ratios to look for trends that could signify compounds that may be involved in signaling. The key strengths of this current approach are the range of sample types that can be analyzed; the lack of need for any complex sample preparation or extractions; the ability to separate and analyze the components of a mixture; the identification of the components detected; and the capability to provide semi-quantitative and potentially quantitative information on the components detected. The main limitation to the methodology relates to the samples themselves. Since the components of specific interest are volatile, and these could easily be lost, or their concentrations altered, it is important that the samples are stored and transported appropriately after their collection. This also means that sample storage and transport conditions are relatively costly. This method can be applied to a variety of samples (including urine, feces, hair and scent-gland odor secretions). These odors consist of complex mixtures, occurring in a range of matrices, and thus necessitate the use of techniques to separate the individual components and extract the compounds of biological interest.

Introduction

Very little is known about the chemical changes underpinning the olfactory signals in animals¹, also because of methodological challenges in recording and quantifying volatile chemical profiles of odors². There are several potential pitfalls when working with highly complex, chemical matrices; these include when sampling and analyzing the odor samples³.

At the Rosalind Franklin Science Center, University of Wolverhampton, we are undertaking the analysis of odors and scent-marks to understand how they may be used by animals. We combine semiochemistry with behavioral ecology, endocrinology and cytology to improve our understanding of the role played by olfactory signals in animal communication.

We have developed a methodology and then analyzed odors and markings from a variety of species including several non-human primates (i.e., crowned lemurs, red-ruffed lemurs, Japanese macaques, olive baboons, chimpanzees) and other mammals (i.e., cats, cows). We have collected and analyzed a variety of samples, including urine, feces, hair and scent-gland odor secretions. These odors and scent-marks consist of complex mixtures of compounds and therefore any methodology used for their analysis need to include some form of separatory technique. As illustrated, they also occur in a range of matrices which necessitates the use of techniques to extract the components of interest.

Previous studies by Vaglio et al.⁴ and other authors⁵ used dynamic headspace extraction (DHS) with gas chromatography-mass spectrometry (GC-MS) while direct solvent extraction⁶ and complex solvent extractions⁷ have also been used. Particularly, dynamic headspace sampling involves purging the headspace with a known volume of inert gas that ultimately removes all volatile compounds with the exception of those showing a strong affinity for the sample matrix (for example, polar compounds in aqueous samples).

For the current methodology, we have adopted the technique of headspace solid-phase microextraction (HS-SPME) coupled with GC-MS. In particular, we have developed and enhanced the methodology already used by Vaglio et al. in its previous GC-MS laboratory^8,9,10.

Solventless extraction techniques are very effective for analyzing small, highly volatile compounds (which otherwise can be lost easily from a sample) because these methods immobilize compounds on a stable, solid phase support. The HS-SPME uses a fiber coated with an adsorbent polymer to capture volatile compounds in the sample headspace or to extract dissolved compounds by immersion in an aqueous biological fluid¹¹. The polymer coating does not bind the compounds strongly, therefore by heating in the injection port of the GC they can be removed. This method is more powerful than solvent extraction techniques and also more effective than DHS.

In the current approach samples are contained within glass vials. These vials are warmed to a temperature of 40 °C to simulate animal body temperature in order to promote the volatile components of the scent-mark to occupy the head-space of the vial. A SPME fiber, coated with 65 µm of polydimethylsiloxane/divinylbenzene (PDMS/DVB) sorbent material, is exposed to the headspace environment and volatile components from the sample are adsorbed onto the fiber. On heating the fiber in the inlet port of a GC-MS, the volatile components are desorbed from the fiber and then separated by the GC. Mass spectral fragmentation patterns are obtained for each component using the MS. By comparison of these mass spectra against mass spectral databases, it can be possible to tentatively identify the components of the scent-mark. Through the use of an auto-sampler, we are able to analyze multiple samples in batches in a consistent manner.

Given that each type of SPME fiber has a different affinity with polar chemicals, the fiber is usually chosen depending on the polarity and/or molecular weight of the target chemical compounds. In addition, the GC conditions are changed depending on the type of GC column and the characteristics of the target chemical compounds.

This technique allows the semi-quantitative analysis of the volatile components of scent-markings by enabling the separation and tentative identification of the components in the sample, followed by the analysis of peak area ratios to look for trends that could signify components of the scent-marking that may be involved in signaling.

The key strengths of this current approach are:

• The range of sample types that can be analyzed.
• No complex sample preparation or extractions are required.
• The ability to analyze volatile components.
• The ability to separate the components of a mixture.
• To be able to identify the components detected.
• The ability to provide semi-quantitative and potentially quantitative information on the components detected.

Protocol

1. Sample collection

1. Sample odors that are one of the following:
   1. Collect spontaneously released by habituated study subjects (e.g., zoo primates) via scent-marking on sterile filter paper (e.g., scent-gland odor secretions) or directly into vials (e.g., urine).
   2. Collect by rubbing sterile cotton swabs after training study subjects by using positive reinforcement training.
   3. Collect by rubbing sterile cotton swabs after sedation of study subjects.
2. Place samples into sterile 10 mL screw-capped clear glass vials, and seal with screw topped caps incorporating FTFE/silicone septa. Immediately store them at -20 °C.
   NOTE: It is vital to use clean personal protective equipment, such as nitrile gloves; change them frequently; avoid direct skin contact with samples and vials. It is preferred to use brand-new vials; however, in case of used vials, it is vital to pre-clean the vials, and then use the same protocol.
3. Take environmental blanks every time scent-marks are collected. For example, collect the sampling media (e.g., filter paper or swab) and a headspace vial that are exposed to the environment while sampling is undertaken.

2. Sample preparation

1. Prepare samples in the field by cutting with a blade an approximate 10 mm square from a scent-marked filter paper, or the head of the swab, and placing it in a 10 mL screw-topped head-space vial.
2. After each sample has been prepared, dispose or clean the blade used to cut the sampling media using an appropriate antibacterial wipe and/or alcohol and dry thoroughly.
3. Store all samples at -20 °C.
   NOTE: Preferred at -20 °C or otherwise as low as practically possible in the field.

3. Preparation for analysis

1. Remove samples from the freezer and allow to warm naturally to room temperature for at least 1 h.
2. Set up the analytical method on the GC-MS as follows:
   1. For SPME analysis conditions, follow the manufacturer's directions to condition SPME fibers before first use: fiber pre-condition (260 °C for 5 min), sample incubation (40 °C for 2 min), extraction time (15 min), desorption time (2 min), and fiber post-condition (260 °C for 20 min).
   2. Use the following GC conditions: column (HP5-MS 30 m x 0.25 mm; 0.25 µm), injector temperature (270 °C), flow rate (1 mL/min), injection mode (splitless), GC oven profile (45 °C for 2 min; 4 °C/min to 170 °C; 20 °C/min to 300 °C), MSD transfer line (280 °C).
      NOTE: To improve between sample retention time consistency the analytical method is retention time locked).
   3. Use the following MSD conditions: solvent delay (2.5 min) and scan range (29 to 400 amu).
      NOTE: A range of 10 to 400 was used in previous protocols⁴.
3. Ensure that the purge gas supply to the fiber conditioning unit is turned on.
   ​NOTE: It is vital that the SPME assembly is correctly installed in the auto-sampler and that it is aligned to the auto-sampler trays, fiber conditioning unit and GC inlet port. Incorrect alignment could result in damage or destruction of the SPME fiber.

4. Analysis

1. Place an empty headspace vial (to act as a system blank) in the first position of the GC-MS auto-sampler tray. Place the environmental blank in the second position of the auto-sampler tray. Place the samples for analysis in the subsequent positions of the auto-sample tray.
2. Create an analytical sequence to analyze each sample within the sample tray.
   1. In the MassHunter home screen, select Sequence | Load sequence.
   2. Complete the sequence table for all blanks and samples by inserting the appropriate information. Save the completed sequence table.
      NOTE: The exact information for the sequence table will be dependent on the laboratories formatting for the table. Minimum information would normally include sample type, sample name, vial location and number, analytical method and data file location and name (allocation of a data file name that matches the sample name aids future data processing). Additional samples can be added to the sequence during the analysis.
3. Run the sequence by selecting Sequence | Run sequence.
4. After analysis return samples to the freezer as soon as possible.
   ​NOTE: it may be possible to reanalyze samples, but it should be noted that some volatile components may have been totally extracted during the initial analysis and some compounds may have undergone thermal and bacterial decomposition at 40 °C, thus the resulting chromatogram may not be thoroughly representative of the original scent-marking.

5. Data analysis

NOTE: Initial data analysis includes the integration of chromatograms to obtain retention time and peak area data together with tentative identification of peaks using ChemStation software and NIST (National Institute of Standards and Technology) mass spectral databases, version MSD F.01.01.2317. Data analysis can be carried out either manually or through a semi-automated method. If the semi-automated method is used then it is sometimes beneficial to undertake a degree of manual data analysis to verify tentative identifications.

1. Open the data file by clicking on the appropriate file in the left-hand navigation bar. The total ion chromatogram (TIC) will be displayed in the top window of the data analysis screen.
2. To integrate the TIC using the RTE integrator, select Chromatogram | Integrate.
3. Adjust integration parameters so that peaks that are greater than 3 x baseline noise are integrated. Select Chromatogram | MS Signal Integration Parameters. In the output box adjust the minimum peak area as appropriate (1.0 produces acceptable results in our examples).
4. To identify peaks and generate a summary report, select Export Reports | Library search results report to XLS.
   NOTE: The spectral libraries to be searched together with the number of library matches to be displayed need to be preset within the software before a library search can be undertaken.
5. The resulting spreadsheet report contains integration data for each peak and a tentative spectral library match to assign identity. Typically, the library quality/library match should be >80 to accept the tentative identification. Save the spreadsheet.
6. Identify a peak directly from the TIC.
   1. Choose the peak of interest.
   2. If the peak is small, zoom in by drawing a box around the peak by holding the left hand mouse button down, stretch the box over the peak and release.
   3. Place the cursor line so that it is at the highest point of the peak (or just after).
   4. Double click the right hand mouse button and the mass spectrum for the peak will appear in the lower window of the data analysis screen.
   5. To search the spectral library, move the cursor anywhere in the spectral window and double click the right hand mouse button. The library search results will appear in a new window.
   6. To remove the background noise from a spectrum of interest, first double click the right hand mouse button on the peak in question. Then double click the right hand mouse button in an area with no peaks immediately in front of the peak of interest. Select Chromatogram | Subtract spectra. The subtracted spectrum will display in the lower window of the data analysis screen and will display '(-)' next to the SCAN data in the window header.

Results

Following this protocol, we tentatively identified a total of 32 volatile chemical compounds from the analysis of 14 ano-genital scent-marks spontaneously released on filter paper by red-ruffed lemurs (Varecia variegata rubra) and compared odor profiles with features of the signaler¹². Naturally occurring volatile compounds, such as hydrocarbons, terpenes, terpene alcohols and ketones, were present within these profiles and included compounds that had previously been found to act as sex p...

Discussion

The use of control samples, both environmental controls created at the time of sample collection and system blanks, are crucial for the interpretation of the scent-mark samples. Any peaks attributed to the sampling environment or the instrumental system need to be excluded from scent-mark samples so that only the peaks of interest are included in any interpretation. These controls can also play a role in assessing and monitoring the 'health' of the instrumentation.

The protocol includes steps ...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
10 mL autosampler vials	Agilent	5188-5392	10 ml screwtop vials with
18 mm vial caps	Agilent	8010-0139	Magnetic with PTFE/silicone septa
Autosampler	Agilent	GC120 PAL autosampler
Capillary column	Agilent	HP5-MS	30 m x 0.25 mm; 0.25 µm
Data analysis software	Agilent	-	ChemStation
Gas Chromatograph	Agilent	7890B
Inlet septa	Agilent	5182-3442	Merlin microseal
Mass Selective Detector	Agilent	5977A
Reporting software	Microsoft	-	Excel
Spectral library	NIST	-	NIST/EPA/NIH Mass Spectral Library
Spectral library search program	NIST	-	MS Search v.2.2
Splitless Inlet liner	Agilent	5190-4048
SPME fibres	Agilent	SU57345U	65 µm PDMS/DVB fibre